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Improved Automated Reaction Mapping

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Experimental Algorithms (SEA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6630))

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Abstract

Automated reaction mapping is an important tool in cheminformatics where it may be used to classify reactions or validate reaction mechanisms. The reaction mapping problem is known to be NP-Complete and may be formulated as an optimization problem. In this paper we present three algorithms that continue to obtain optimal solutions to this problem, but with significantly improved runtimes over the previous CCV algorithm. Our algorithmic improvements include (a) the use of a fast (but not 100% accurate) canonical labeling algorithm, (b) name reuse (i.e., storing intermediate results rather than recomputing), and (c) an incremental approach to canonical name computation. Experimental results on chemical reaction databases demonstrate our 2-CCV NR FDN algorithm usually performs over ten times faster than previous fastest automated reaction mapping algorithms.

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Authors and Affiliations

  1. Colorado School of Mines, USA

    Tina Kouri & Dinesh Mehta

Authors
  1. Tina Kouri
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  2. Dinesh Mehta
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Editors and Affiliations

  1. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  2. Division of Economics and Business, Colorado School of Mines, Engineering Hall 310, 816 15th Street, 80401, Golden, CO, USA

    Steffen Rebennack

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© 2011 Springer-Verlag Berlin Heidelberg

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Kouri, T., Mehta, D. (2011). Improved Automated Reaction Mapping. In: Pardalos, P.M., Rebennack, S. (eds) Experimental Algorithms. SEA 2011. Lecture Notes in Computer Science, vol 6630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20662-7_14

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  • DOI: https://doi.org/10.1007/978-3-642-20662-7_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20661-0

  • Online ISBN: 978-3-642-20662-7

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